What We Know About Tuberculosis Transmission: An Overview

Churchyard, Gavin J.; Kim, Peter; Shah, Neha; Rustomjee, Roxana; Gandhi, Neel R.; Mathema, Barun; Dowdy, David W.; Kasmar, Anne; Cárdenas, Vicky

doi:10.1093/infdis/jix362

Cited by 240 publications

(180 citation statements)

References 47 publications

(46 reference statements)

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“…Especially, IFN‐γ secreted by Th1 cells can promote the large secretion of macrophage inflammatory protein‐1α (MIP‐1α) and RANTES, thus chemotactic monocytes clustering around the TB foci to phagocytose, and eliminate MTB. However, MTB can inhibit the macrophage response to IFN‐γ to survive in host cells, so IFN‐γ is important in the defence of MTB infection . In the process of TB infection, IFN I pathway plays a crucial role in anti‐TB infection, but it is also a double‐edged sword, and researches show that IFN I including IFN‐α and IFN‐β can help to promote the TB infection by interfering Th1 immune response and suppressing cytokines such as IL‐1β, TNF‐α and IFN‐γ.…”

Section: Discussionmentioning

confidence: 99%

STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection

Zhang

et al. 2020

J Cellular Molecular Medi

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Tuberculosis (TB) is a severe infectious disease that seriously endangers human health. The immune defence mechanism of the body against TB is still unclear. The purpose of this study was to find the key molecules involved in the immune defence response during TB infection, and provide reference for the treatment of TB and further understanding of the immune defence mechanism of the body. Data from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83456 were downloaded from GEO data sets for analysis, and a total of 192 differentially expressed genes were screened out. Most of these genes are enriched in the interferon signalling pathway and are defence response–related. We also found that STAT1 plays an important role in the immune defence of TB infection and it is one of the key genes related to interferon signalling pathway. STAT1‐related molecules including hsa‐miR‐448, hsa‐miR‐223‐3p, SAMD8_hsa_circRNA 994 and TWF1_hsa_circRNA 9897 were therefore screened out. Furthermore, expression levels of hsa‐miR‐448 and hsa‐miR‐223‐3p were then verified by qRT‐PCR. Results showed that both hsa‐miR‐448 and hsa‐miR‐223‐3p were down‐regulated in plasma from patients with pulmonary TB. Taken together, our data indicate that an mRNA‐miRNA‐circRNA interaction chain may play an important role in the infection of MTB, and STAT1 and related molecules including hsa‐miR‐223‐3p, has‐miR‐448, SAMD8_hsa_circRNA994 and TWF1_hsa_circRNA9897 were identified as potential biomarkers in the development of active TB.

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Section: Discussionmentioning

confidence: 99%

STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection

Zhang

et al. 2020

J Cellular Molecular Medi

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“…Tuberculosis (TB) kills more people than any other infectious disease and halting transmission of Mycobacterium tuberculosis is essential to reducing the global burden of disease. However, in high-incidence settings, it is unknown where and between whom the majority of transmission occurs 2–4 and therefore where to focus interventions. Patterns of M. tuberculosis genetic and genomic variation are frequently used to identify potential recent transmission events.…”

Section: Introductionmentioning

confidence: 99%

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

Walter

Colijn

Cohen³

et al. 2019

Preprint

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20Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important 21 human pathogens and to inform public health interventions. Yet there is no current consensus on how to measure 22 genomic variation. We investigated the effects of variant identification approaches on transmission inferences for 23 M. tuberculosis by comparing variants identified by five different groups in the same sequence data from a clonal 24 outbreak. We then measured the performance of commonly used variant calling approaches in recovering 25 variation in a simulated tuberculosis outbreak and tested the effect of applying increasingly stringent filters on 26 transmission inferences and phylogenies. We found that variant calling approaches used by different groups do 27 not recover consistent sets of variants, often leading to conflicting transmission inferences. Further, performance 28 in recovering true outbreak variation varied widely across approaches. Finally, stringent filters rapidly eroded the 29 accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. We 30 conclude that measurements of genetic distance and phylogenetic structure are dependent on variant calling 31 approach. Variant calling algorithms trained upon true sequence data outperform other approaches and enable 32 inclusion of repetitive regions typically excluded from genomic epidemiology studies, maximizing the 33 information gleaned from outbreak genomes. 34 35 42 settings, it is unknown where and between whom the majority of transmission occurs 2-4 and therefore where to 43 focus interventions. Patterns of M. tuberculosis genetic and genomic variation are frequently used to identify 44 potential recent transmission events. M. tuberculosis isolates that share a genotype (RFLP, spoligotype, or MIRU-45 VNTR) 5-7 , or whose whole genome sequences are within a given genetic distance [8][9][10][11] , are considered clustered and 46 potentially epidemiologically linked. Phylogenies inferred from outbreak variation may reveal patterns of 47 relatedness within and between clusters 11-13 . Finally, transmission trees integrate epidemiological and phylogenetic 48 information to capture probable transmission histories, chains of who infected whom 14,15 . Predicted transmission 49 links have been used to infer the likely location and/or timing 16,17 of transmission, to identify risk factors for 50 transmission and high risk populations 18 , to distinguish between acquired (primary) and transmitted drug 51 resistance 19 , and to declare an outbreak over 20 . 52 3 Transmission inferences in molecular epidemiology for M. tuberculosis and other pathogens rely on the 53 high-quality measurement of genetic variation from sequence data. However, there is no consensus on how to 54 measure pathogen genomic variation, and studies frequently employ different sequence quality control measures, 55 mapping algorithms, variant callers, and variant filters 21 . Further, the performance of variant calling...

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“…Introduction 24 Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), latently 25 infects roughly one-third of the world's population and causes 1-2 million deaths per year. The 26 current paradigm of acute infection is that after an actively infected person aerosolizes infectious 27 Mtb-containing particles, a naïve individual inhales the bacteria that then traverse the respiratory 28 tree to ultimately be phagocytosed by alveolar macrophages (Churchyard et al, 2017;Cohen et 29 al., 2018). While this model can account for pulmonary TB, it is insufficient to explain some 30 extrapulmonary forms of TB initiated by oropharyngeal infection and lacking evidence of 31 concurrent pulmonary disease.…”

Section: Running Title: Sr-b1 Is An Airway M Cell Receptor For Mtb Esmentioning

confidence: 99%

Identification of scavenger receptor B1 as the airway microfold cell receptor forMycobacterium tuberculosis

Khan

Nair

Ruhl

et al. 2019

Preprint

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Mycobacterium tuberculosis (Mtb) can enter the body through multiple routes, 15 including via specialized transcytotic cells called microfold cells (M cell). However, the 16 mechanistic basis for M cell entry remains undefined. Here, we show that M cell transcytosis 17 depends on the Mtb Type VII secretion machine and its major virulence factor EsxA. We identify 18 scavenger receptor B1 (SR-B1) as an EsxA receptor on airway M cells. SR-B1 is required for Mtb 19 binding to and translocation across M cells in mouse and human tissue. Together, our data 20 demonstrate a previously undescribed role for Mtb EsxA in mucosal invasion and identify SR-B1 21 as the airway M cell receptor for Mtb. 22 65 receptor class B type I (SR-B1). SR-B1 is enriched on mouse and human M cells both in vitro and 66 in vivo. Loss of SR-B1 reduces EsxA and Mtb binding to M cells, and prevents Mtb translocation 67 109 HBE/Raji B transwells, we observed that Mtb translocated across Caco-2/Raji B transwells in a 110 T7SS dependent manner (Fig. 1G). Taken together, these data show that the Mtb T7SS is necessary 111 for both binding to and translocation across M cells in vitro. 112 EsxA is sufficient to mediate binding to and translocation across M cells in vitro 113 Scavenger receptor class B type 1 binds EsxA and is expressed on M cells in vitro131 Because EsxA bound directly to the surface of M cells, we hypothesized that EsxA may 132 engage a cell surface receptor. To affinity purify cell surface binding proteins we performed a 133 modified co-immunoprecipitation experiment using either EsxA or transferrin crosslinked to the 134 TriCEPS reagent, a molecule that allows for the covalent cross-linking of a ligand and its receptor 135 157

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What We Know About Tuberculosis Transmission: An Overview

Cited by 240 publications

References 47 publications

STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection

STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

Identification of scavenger receptor B1 as the airway microfold cell receptor forMycobacterium tuberculosis

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